Natural language processing to identify substance misuse in the electronic health record
Authors: Tyne A Riddick, Esther K Choo
Journal: The Lancet Digital Health (2022)

Screening for unhealthy drug use among adults is the US Preventative Services Task Force standard for health-care providers.1Krist AH Davidson KW Mangione CM et al.Screening for Unhealthy Drug Use: US Preventive Services Task Force Recommendation Statement.JAMA. 2020; 323: 2301-2309Crossref PubMed Scopus (105) Google Scholar Screening can help identify those with substance use disorders who require referral to treatment, and also those at increased risk for harmful alcohol or drug use, who might still avoid experiencing a negative health sequela. As with many screening recommendations,2Yarnall KSH Pollak KI Østbye T Krause KM Michener JL Primary care: is there enough time for prevention?.Am J Public Health. 2003; 93: 635-641Crossref PubMed Scopus (1305) Google Scholar substance use screening is challenging to perform consistently with the scarce time health-care providers have for patient care. Improving the identification of substance use disorders, therefore, is a popular area for the application of digital technologies.3Marsch LA Campbell A Campbell C et al.The application of digital health to the assessment and treatment of substance use disorders: The past, current, and future role of the National Drug Abuse Treatment Clinical Trials Network.J Subst Abuse Treat. 2020; 112S: 4-11Summary Full Text Full Text PDF PubMed Scopus (34) Google Scholar The study by Majid Afshar and colleagues in this issue of The Lancet Digital Health4Afshar M Sharma B Dligach D et al.Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study.Lancet Digit Health. 2022; 4: e426-e435Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar sought to build a natural language processing tool to automate screening efforts from the free text format of the electronic health record (EHR) in the first 24 h of hospitalization. The authors successfully created a convolutional neural network that screened and identified multiple types of substance misuse (alcohol, opioid, and non-opioid misuse). The study relied on two presumptions: in the derivation phase, that clinician notes are independent of a referent hospital-wide substance misuse screening tool; second, in lieu of a standardised screening tool within their external validation site, reliability of the medical reviewers who served instead as the referent standard. The clinical notes were processed using a programme that recognises words that align with documented entities in the Unified Medical Language System, which helped organise the mapped terms as concept unique identifiers (CUIs). Using multilabel and auxiliary task learning as two approaches to building the classifier, the authors examined several model architectures that used CUIs as inputs for supervised learning. Ultimately the multilabel convolutional neural network had the best predictive metrics with a mean AUROC of 0·97 (95% CI 0·96–0·98) across alcohol, opioid, and non-opioid misuse, and in external validation, AUROC of 0·88 (0·86–0·90) for alcohol and 0·94 (0·92–0·95) for opioid misuse. Overall, the work generated a remarkably high-performing tool that addresses a clear gap in clinical assessment and care. The novelty and high performance of this work does require some counterbalancing caution. Any screening tool that relies on clinician charting must acknowledge that such documentation could be highly biased and potentially reproduced from earlier impressions (ie, when one person introduces stigmatising language, this tends to echo through the documentation thereafter).5Wang MD Khanna R Najafi N Characterizing the source of text in electronic health record progress notes.JAMA Intern Med. 2017; 177: 1212-1213Crossref PubMed Scopus (59) Google Scholar As an illustrative example, a study6Sun M Oliwa T Peek ME Tung EL Negative patient descriptors: documenting racial bias in the electronic health record.Health Aff (Millwood). 2022; 41: 203-211Crossref PubMed Scopus (68) Google Scholar of the electronic health records of more than 18 000 patients found that Black patients had 2·54 times the odds of having at least one descriptor that could be perceived as negative in the history and physical notes compared with White patients. Such disparities might occur in screening, formal assessment, charting, and clinical judgment and approach. This was particularly true for inpatients, the very cohort used in the current study by Afshar and colleagues. Thus, any digital system relying on EHR documentation risks amplifying, solidifying, and validating biases that are embedded within. Indeed, the study found that their methodology yielded a higher false-discovery rate for Black patients. What one puts into a model, even with protections against bias, determines what one gets out, and risks compounding inequities in assessment and treatment. Furthermore, from the patient perspective, automating a scan for substance misuse instead of the standard of care (asking the patient directly) can be problematic, and could feel like a betrayal of current and previous providers and exacerbate existing concerns about being labelled in their medical chart even before a patient has decided to engage with the health-care system around the topic.8McNeely J Kumar PC Rieckmann T et al.Barriers and facilitators affecting the implementation of substance use screening in primary care clinics: a qualitative study of patients, providers, and staff.Addict Sci Clin Pract. 2018; 13: 8Crossref PubMed Scopus (123) Google Scholar Trust is paramount to screening that can lead to successful diagnoses and a therapeutic relationship and is the ideal pairing with technical virtuosity. Machine learning has tremendous potential in tackling the challenges of health care through technology-driven, systematised disease detection. For example, in the field of Alzheimer's disease and related dementias, more than half of patients with dementia remain undiagnosed until a pivotal crisis health event.9Perry-Young L Owen G Kelly S Owens C How people come to recognise a problem and seek medical help for a person showing early signs of dementia: a systematic review and meta-ethnography.Dementia. 2018; 17: 34-60Crossref PubMed Scopus (38) Google Scholar Natural language processing holds promise to identify earliest Alzheimer's symptoms, such as subtle language impairments,10de La Fuente Garcia S Ritchie CW Luz S Artificial intelligence, speech, and language processing approaches to monitoring Alzheimer's disease: a systematic review.J Alzh Dis. 2020; 78: 1547-1574Crossref PubMed Scopus (78) Google Scholar to allow patients and families to receive necessary care earlier in the disease process. However, an Alzheimer's disease diagnosis is often the result of sought-after care by either the person or someone close to them due to puzzling symptoms or decline—contrast this with substance misuse screening performed in the background, without explicit notification. How patient perceptions and motivations might intersect with this work represent opportunities in what will certainly be productive future work in this realm. Future research must centre on patient perspectives (eg, by including patients with substance misuse histories or patient advocates in early work of this nature) to build systems and processes that minimise, rather than amplify, biases that exist within health systems, particularly for those who are most vulnerable to their harms. EKC is the co-founder of Equity Quotient, a consulting group that advises companies on building an equitable organizational environment. TAR declares no competing interests. Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning studyWe developed a novel and accurate approach to leveraging the first 24 h of EHR notes for screening multiple types of substance misuse. Full-Text PDF Open AccessTurning the tide on the opioid crisisIn February 2022, findings of the Stanford–Lancet Commission on the North American Opioid Crisis were published, presenting an analysis of seven key domains related to the crisis including regulatory failures, lack of addiction-related health and social care services, and the slow pace of innovation. Among the Commission's recommendations were promoting opioid stewardship (ie, risk-reduction interventions) such as prompts in electronic prescribing systems that nudge clinicians towards safer prescribing, and tasking a federal agency with leading innovative projects, including developing machine-learning algorithms to predict risk of addiction and overdose. Full-Text PDF Open Access